FairHealthGrid: A Systematic Framework for Evaluating Bias Mitigation Strategies in Healthcare Machine Learning
Bibliographic record
Abstract
The integration of machine learning (ML) into healthcare demands rigorous fairness assurance to prevent algorithmic biases from exacerbating disparities in treatment. This study introduces FairHealthGrid, a systematic framework for evaluating bias mitigation strategies across healthcare ML models. Our framework combines grid search with a composite fairness score, incorporating fairness metrics weighted by risk tolerances. As output, we present a trade-off map concurrently evaluating accuracy and fairness, categorizing solutions (model + bias mitigation) into the following regions: Win-Win, Good, Poor, Inverted, or Lose-Lose. We apply the framework on three different healthcare datasets. Results reveal significant variability across different healthcare applications. The framework identifies model-bias mitigation for balancing equity and accuracy, yet highlights the absence of universal solutions. By enabling systematic trade-off analysis, FairHealthGrid allows healthcare stakeholders to audit, compare, and select ethically aligned ML models for specific healthcare applications—advancing towards equitable AI in healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".